Stress Intensity Factors for Pressurized Pipes with an Internal Crack: The Prediction Model Based on an Artificial Neural Network

<p>Uncertainty Analysis of Artificial Neural Network (ANN) And Support Vector Machine (SVM) Models in Predicting Monthly River Flow (Case Study: Ghezelozan River)</p>

In the chemical industry is important to control the process in order to guarantee the quality and repeatability of the final product. Using sensors in the industrial plant allows a large volume of data to be captured regarding the process. These data can be used for modelling to better understanding and predict the properties of the product in the process. In this work, two types of Artificial Neural Networks (ANN) and the hybrid model Adaptive Neuro Fuzzy Inference System (ANFIS) were used to predict the density of polystyrene along the styrene polymerization process. The dataset used was extracted from the batch of polymerization reactions performed in open-loop, manual control and closed-loop and monitored in each 5 seconds. The Feedforward and Elman ANN has coefficient of correlation (R) equal 94.2%. However, the best topology obtained to Feedforward ANN presents 2 hidden layers and error index RMSE (Root Mean Squared Error) equal to 2.69x10-2. The Elman ANN presents only 1 hidden layer and RMSE of 3.39x10-2. The ANFIS model, in turn, presented R equal to 91% and RMSE of 0.2123. Therefore, ANFIS model did not prove to be the most adequate for the prediction of the polystyrene density in the studied process.Polymerization process pose significant challenges to the industrial community as it is difficult to control with high nonlinearity behaviour and fast dynamic response. The monitoring and control of polymer processes guarantee to the final product the qualities required by the market. Muhammad and Aziz (2017), for instance, studied the production of low density polyethylene (LDPE) and presented a review of the control strategies developed for the LDPE process. The strategies presented were developed in tubular and autoclave reactors and highlights the importance of nonlinear control in polymerization process.The use of sensors to monitor production allows a large volume of process data to be collected. Therefore, it is necessary to construct mathematical models of prediction to interpret and correlate significant patterns, indispensable to assist in the management of decisions and risk analysis.The development of phenomenological models for polymerization is complex and requires deep knowledge about the processes involved in each step. Modelling using artificial intelligence is a strategy that can provide valuable information about the process and allows the construction of an intelligent model capable of predicting process response based on parameters provided. ANN and ANFIS are artificial intelligence tools that can be used to build predictive models. In supervised learning, the data are presented to the network and its main objective is to provide a model that correctly correlates the pairs inputs - outputs of the problems. The use of ANN and ANFIS to predict the density of the polystyrene produced in the process becomes attractive since it is a type of non-linear modelling.Jumari and Mohd-Yusof (2017) presents models to measure melt flow index (MFI) in industrial polypropylene loop reactors using first principle (FP) model and ANN model. The authors state that the prediction of the ANN model is more accurate compare to the MFI calculated by the FP model. Furthermore, the CPU time recorded that ANN model is much faster than FP model.This work aims to develop direct and recursive ANN and ANFIS models from a set of experimental data from a controlled styrene polymerization plant capable of predicting the density of the product satisfactorily.

This article compares two modeling approaches for optimal operation of a turbo chiller installed in an office building: (1) a machine learning model developed with artificial neural network (ANN) and (2) a hybrid machine learning model developed with the ANN model and available physical knowledge of the chiller. Before developing the ANN model of the chiller, the authors used Gaussian mixture model in order to check the validity of measured data. Then, the hybrid model was developed by combining the ANN model and physics-based regression equations from the EnergyPlus engineering reference. It was found that both the ANN and hybrid ANN model are satisfactory to predict the chiller’s power consumption: mean bias error (MBE) = −2.63%, coefficient of variation of the root mean square error (CVRMSE) = 8.05% by the ANN model; MBE = −3.99%, CVRMSE = 11.98% by the hybrid ANN model. However, the hybrid model requires fewer inputs (four inputs) than the ANN model (eight inputs). The energy savings of both models are similar coefficient of performance (COP) = 4.32 by the optimal operation of the ANN model; COP = 4.44 by the optimal operation of the hybrid ANN model. In addition, the hybrid ANN model can be applied where the ANN model is unable to provide accurate predictions.

Chapter 4 - Concepts, procedures, and applications of artificial neural network models in streamflow forecasting

Computational intelligent techniques for predicting optical behavior of different materials

Geopolymer concrete (GPC) is an efficient alternative to traditional construction materials, utilizing amorphous or semicrystalline waste–based binders. It enhances durability than Portland cement, remaining environment friendly and cost‐effective, while promoting waste reuse. Despite extensive use and research, predicting concrete strength based on the mixed chemical composition of constituents remains challenging due to consideration of limited variables, small dataset sizes, and traditional models struggling with nonlinearity. The primary objective is to develop an artificial neural network (ANN) model to enhance the accuracy of compressive strength (C‐S) predictions of GPC, which is crucial for the sustainable use of waste‐based binders in construction. For this, the study incorporates a comprehensive dataset of 1018 experimental data points of binary and ternary GPC from 43 research sources addressing these issues. Thirteen input parameters, including three major geopolymer binder’s oxide compositions (viz, SiO2, CaO, and Al2O3), are considered to generalize the model across various binder types. With all these, an ANN model was developed to handle the inherent nonlinearity of input parameters, validated through fivefold cross‐validation. The relationship between individual parameters and C‐S is analyzed. Additionally, regression models (viz., linear regression (LR), Lasso regression, Ridge regression, and XGBoost regressor) were developed to thoroughly assess the performance of the ANN model in comparison to the conventional regression model. The ANN model, superior to other regression techniques, excels in recognizing nonlinear correlations between features and an objective variable, a challenge for traditional regression models. Various metrics assess model performance (e.g., mean absolute error (MAE), root mean square error (RMSE), mean square error (MSE), R‐squared (R2)), revealing the ANN model’s superiority over regression models. The ANN model’s proficiency in nonlinear correlations contributes to its effectiveness, yielding low MAE ( = 2.58), RMSE ( = 4.13), and MSE ( = 17.86) values, and high (R2 = 0.93) value, signifying minimal deviation between predicted and actual C‐S values. By focusing on the oxide composition of binders, the model is more generalized, making it applicable across different binder types and not limited to specific materials. Also, the parametric analysis confirmed the ANN’s ability to capture the effects of input parameters, offering a comprehensive prediction tool. This study highlights the potential of employing the ANN model for predicting material behavior, offering a resource‐efficient approach, and showcasing the viability of mixed GPC for sustainable industrial waste utilization. Future research directions include exploring the model’s application under varying environmental conditions and expanding the dataset to enhance its diversity and representativeness.

Groundwater level fluctuation modeling is a prime need for effective utilization and planning the conjunctive use in any basin.The application of Artificial Neural Network (ANN) and hybrid Wavelet ANN (WANN) models was investigated in predicting Groundwater level fluctuations. The RMSE of ANN model during calibration and validation were found to be 0.2868 and 0.3648 respectively, whereas for the WANN model the respective values were 0.1946 and 0.1695. Efficiencies during calibration and validation for ANN model were 0.8862 per cent and 0.8465 per cent respectively, whereas for WANN model were found to be much higher with the respective values of 0.9436 per cent and 0.9568 per cent indicating substantial improvement in the model performance. Hence hybrid ANN model is the promising tool to predict water table fluctuation as compared to ANN model.

• We modelled rainfall using multiple regression and Artificial Neural Networks for Victoria, Australia. • We examined combined lagged months of ENSO and IOD as potential predictors of spring rainfall. • Spring rainfall was predicted three years in advance with the developed models using ENSO–IOD lagged months. • ANN models proved to be more accurate than multiple regression models in terms of long term predictions. In this study, the application of Artificial Neural Networks (ANN) and Multiple regression analysis (MR) to forecast long-term seasonal spring rainfall in Victoria, Australia was investigated using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential predictors. The use of dual (combined lagged ENSO–IOD) input sets for calibrating and validating ANN and MR Models is proposed to investigate the simultaneous effect of past values of these two major climate modes on long-term spring rainfall prediction. The MR models that did not violate the limits of statistical significance and multicollinearity were selected for future spring rainfall forecast. The ANN was developed in the form of multilayer perceptron using Levenberg–Marquardt algorithm. Both MR and ANN modelling were assessed statistically using mean square error (MSE), mean absolute error (MAE), Pearson correlation ( r ) and Willmott index of agreement ( d ). The developed MR and ANN models were tested on out-of-sample test sets; the MR models showed very poor generalisation ability for east Victoria with correlation coefficients of −0.99 to −0.90 compared to ANN with correlation coefficients of 0.42–0.93; ANN models also showed better generalisation ability for central and west Victoria with correlation coefficients of 0.68–0.85 and 0.58–0.97 respectively. The ability of multiple regression models to forecast out-of-sample sets is compatible with ANN for Daylesford in central Victoria and Kaniva in west Victoria ( r = 0.92 and 0.67 respectively). The errors of the testing sets for ANN models are generally lower compared to multiple regression models. The statistical analysis suggest the potential of ANN over MR models for rainfall forecasting using large scale climate modes.

In this study, the impact performance of carbon fiber‐reinforced polymer (CFRP) composites under low‐speed impact conditions was investigated using a data‐driven approach. Both material properties and impact parameters were determined through experimental methods and finite element (FE) analysis. FE analysis was conducted on CFRP composite structures to generate impact force and absorbed energy datasets. Various impact conditions, such as impactor height (0.5–1.25 m), impactor shape (flat, truncated cone, bullet, and cone), and composite plate thickness (1–4 mm), were incorporated into an artificial neural network (ANN) model to predict the impact behavior of CFRP composites. Using the optimal plate thickness identified from the data‐driven model, CFRP plates were fabricated using vacuum‐assisted resin transfer molding and tested under different impactor shapes and heights using drop impactors. The FE analysis revealed that increasing the impactor height improved the impact force by 37.8% and the absorbed energy by 178%. The impactor shape also significantly influenced the results, with a flat‐to‐cone‐shaped impactor increasing the impact force and absorbed energy by 167.2% and 440%, respectively. Additionally, the plate thickness analysis showed that a 2 mm plate provided optimal impact force and absorbed energy, with values of 1.09 kN and 8.12 J, respectively. The prediction of the force and energy experienced by CFRP material under different impact conditions was validated using root mean squared error (RMSE), mean square error (MSE), mean absolute error (MAE), and R 2 metrics. The model demonstrated excellent performance with the lowest RMSE (0.0118), MSE (0.0003), and MAE (0.0096), indicating that the predicted impact forces closely matched the actual forces. The highest R 2 value (0.9999) suggests that the model accurately captures the variance in impact force across varied impact conditions. Similarly, R 2 values close to one indicate that the model effectively explains the variability in energy absorption, making it highly reliable. The ANN model also showed that predictions for absorbed energy were more accurate than those for impact force under varying impact conditions. Furthermore, the predicted impact force and absorbed energy from the FE analysis and ANN model closely aligned with the experimental results. Damage morphology observations indicated matrix cracking at higher impact velocities, with more significant penetration occurring with cone‐shaped impactors. These findings demonstrate a strong correlation between experimental and numerical outcomes, validating the effectiveness of this combined approach for evaluating the impact resistance of CFRP composites. Highlights The impact performance of CFRP composites under different impact conditions was modeled. An Artificial Neural Network model was developed to predict impact performance. The VARTM method was adopted for the development of optimized CFRP composites. Impactor height and shape were found to be the most influential factors Impact damage areas were related based on impactor height and shape.

A single hidden layer Artificial Neural Network (ANN) model was developed to estimate a machinery energy ratio (MER) indicator, used to characterize and assess mechanization status of potato farms in Iran with a view point of energy expenditure in farm machinery. A wide range of variables of farming activities were examined. Initially, 90 attributes were used as input variables to predict desired MER output. Using regression analysis, 13 inputs were finally selected to model MER. Performance of developed ANN model was evaluated with various statistical measures including the coefficient of determination (R2), mean absolute percentage error (MAPE), mean squared error (MSE) and mean absolute error (MAE). The optimum ANN model had a 13 - 4 - 1 configuration. The values of the optimum model’s outputs correlated well, with R2 of 0.98. Value of MAPE calculated as 0.0001 for best ANN model, which indicate superiority of this model over other prediction models. Sensitivity analyses were also conducted to investigate the effects of each input item on the output value. Since the ANN model can predict this mechanization indicator for a target farming system in Hamadan province of Iran, it could be a good estimator for appraising mechanization of other regional farms. Also it overcomes some of the limitations of using simple data available from local databases as inputs that may contain errors. Key words: Potato, agricultural mechanization, machinery energy ratio, Artificial Neural Network.

The purpose of this study was to validate the use of artificial neural network (ANN) models for predicting quality of life (QOL) after breast cancer surgery and to compare the predictive capability of ANNs with that of linear regression (LR) models. The European Organization for Research and Treatment of Cancer Quality of Life Questionnaire and its supplementary breast cancer measure were completed by 402 breast cancer patients at baseline and at 2 years postoperatively. The accuracy of the system models were evaluated in terms of mean square error (MSE) and mean absolute percentage error (MAPE). A global sensitivity analysis was also performed to assess the relative significance of input parameters in the system model and to rank the variables in order of importance. Compared to the LR model, the ANN model generally had smaller MSE and MAPE values in both the training and testing datasets. Most ANN models had MAPE values ranging from 4.70 to 19.96 %, and most had high prediction accuracy. The ANN model also outperformed the LR model in terms of prediction accuracy. According to global sensitivity analysis, pre-operative functional status was the best predictor of QOL after surgery. Compared with the conventional LR model, the ANN model in the study was more accurate for predicting patient-reported QOL and had higher overall performance indices. Further refinements are expected to obtain sufficient performance improvements for its routine use in clinical practice as an adjunctive decision-making tool.

Artificial neural network algorithms to predict the bond strength of reinforced concrete: Coupled effect of corrosion, concrete cover, and compressive strength

This study deals with the development of an artificial neural network (ANN) and a multiple regression (MR) model that can be employed for estimating the California bearing ratio (CBR) value of some Aegean sands. To achieve this, the results of CBR tests performed on the compacted specimens of nine different Aegean sands with varying soil properties were used in the development of the ANN and MR models. The results of the ANN and MR models were compared with those obtained from the experiments. It is found that the CBR values predicted from the ANN model matched the experimental values much better than the MR model. Moreover, several performance indices, such as coefficient of determination, root-mean-square error, mean absolute error, and variance, were used to evaluate the prediction performance of the ANN and MR models. The ANN model has shown higher prediction performance than the MR model based on the performance indices, which demonstrates the usefulness and efficiency of the ANN model. Thus, the ANN model can be used to predict CBR value of the Aegean sands included in this study as an inexpensive substitute for the laboratory testing, quite easily and efficiently.

Most reports compare artificial neural network (ANN) models and logistic regression models in only a single data set, and the essential issue of internal validity (reproducibility) of the models has not been adequately addressed. This study proposes to validate the use of the ANN model for predicting in-hospital mortality after traumatic brain injury (TBI) surgery and to compare the predictive accuracy of ANN with that of the logistic regression model. The authors of this study retrospectively analyzed 16,956 patients with TBI nationwide who were surgically treated in Taiwan between 1998 and 2009. For every 1000 pairs of ANN and logistic regression models, the area under the receiver operating characteristic curve (AUC), Hosmer-Lemeshow statistics, and accuracy rate were calculated and compared using paired t-tests. A global sensitivity analysis was also performed to assess the relative importance of input parameters in the ANN model and to rank the variables in order of importance. The ANN model outperformed the logistic regression model in terms of accuracy in 95.15% of cases, in terms of Hosmer-Lemeshow statistics in 43.68% of cases, and in terms of the AUC in 89.14% of cases. The global sensitivity analysis of in-hospital mortality also showed that the most influential (sensitive) parameters in the ANN model were surgeon volume followed by hospital volume, Charlson comorbidity index score, length of stay, sex, and age. This work supports the continued use of ANNs for predictive modeling of neurosurgery outcomes. However, further studies are needed to confirm the clinical efficacy of the proposed model.

The development of on-line surface defect detection system for jujubes based on hyperspectral images